#Knowledge-aware Fine-grained Attention Networks with Refined Knowledge Graph Embedding for Personalized Recommendation. This is our Pytorch implementation for the paper:
The code has been tested running under Python 3.8.16. The required packages are as follows:
- torch == 2.0.1
- numpy == 1.24.4
- sklearn == 1.2.2
The hyper-parameter search range and optimal settings have been clearly stated in the codes (see the parser function in src/main.py).
- Train and Test
python main.py
We provide three processed datasets: Book-Crossing, MovieLens-1M, and Last.FM.
We follow the paper " Ripplenet: Propagating user preferences on the knowledge graph for recommender systems" to process data.
| Book-Crossing | MovieLens-1M | Last.FM | ||
|---|---|---|---|---|
| User-Item Interaction | #Users | 17,860 | 6,036 | 1,872 |
| #Items | 14,967 | 2,445 | 3,846 | |
| #Interactions | 139,746 | 753,772 | 42,346 | |
| Knowledge Graph | #Entities | 77,903 | 182,011 | 9,366 |
| #Relations | 25 | 12 | 60 | |
| #Triplets | 151,500 | 1,241,996 | 15,518 |
Wang W, Shen X, Yi B, et al. Knowledge-aware fine-grained attention networks with refined knowledge graph embedding for personalized recommendation[J]. Expert Systems with Applications, 2024: 123710.